Vipul Periwal
About Vipul Periwal
Vipul Periwal is a Senior Investigator at NIH/NIDDK/LBM, focusing on modeling systemic responses to biological changes. He has a strong academic background in Physics and Mathematics from Caltech and Princeton University, and he specializes in computational medicine and complex biological processes.
Work at National Institutes of Health
Vipul Periwal has served as a Senior Investigator at the National Institutes of Health (NIH) within the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) since 2014. He is based in Bethesda, MD, where he leads the Computational Medicine Section. His work focuses on predicting systemic responses to biological perturbations through advanced modeling techniques. He utilizes Bayesian model comparison to address biological variability and prevent data over-fitting, contributing to the field of computational biology.
Education and Expertise
Vipul Periwal holds a Bachelor of Science degree in Physics and Mathematics from the California Institute of Technology (Caltech), where he studied from 1980 to 1983. He furthered his education at Princeton University, earning both a Master of Arts and a PhD in Physics from 1983 to 1988. His academic background provides a strong foundation for his expertise in applying theoretical physics to biological modeling, particularly in understanding complex biological processes.
Background
Before joining NIH, Vipul Periwal held several academic positions. He was an Assistant Professor at Princeton University from 1993 to 2001, where he contributed to research and education in physics. Prior to that, he was a Post-doctoral Fellow at the Kavli Institute for Theoretical Physics from 1988 to 1991. His diverse background in theoretical physics and mathematics has shaped his approach to biological modeling and research.
Research Focus and Contributions
Vipul Periwal's research primarily involves understanding and modeling complex biological processes, including adipose tissue dynamics, islet development, and liver regeneration. He engages in determining molecular interactions using single-cell data to enhance the understanding of disease processes. His work includes predicting transcription factor activity from DNA sequences and analyzing protein structures from sequence alignments, integrating concepts from quantum field theory and string theory into biological contexts.